Data Science and visualisation : introduction
Etienne Côme
October 17, 2024
Data Science ?
The next sexy job
The ability to take data to be able to understand it, to process
it, to extract value from it, to visualize it, to communicate it, that’s
going to be a hugely important skill.
– Hal Varian, Google
Data Science ?
Data science, as it’s practiced, is a blend of Red-Bull-fueled
hacking and espresso-inspired
statistics.
Data science is the civil engineering of data. Its acolytes
possess a practical knowledge of tools & materials, coupled with a
theoretical understanding of what’s possible
– Mike Driscoll, CEO of metamarkets
Drew Conway’s
Data Science Venn Diagram
Data Science ?
A data scientist is someone who can obtain, scrub, explore, model
and interpret data, blending hacking, statistics and machine learning.
Data scientists not only are adept at working with data, but appreciate
data itself as a first-class product.
– Hilary Mason, chief scientist at bit.ly
Data Science?
Talking about data also evokes the
datascientist, this five-legged sheep of data with
statistical, computer skills, perfectly understanding
the business stakes of the company… Is he also a
fantasy of the ambient discourse on big data ?
Data Science?
While there may exist profiles that come close to this
description, reality shows most often that datascience, like science in
general, does not happen alone but in a group.(…)
Another little-known fact about the datascientist is that he is
first and foremost a craftsman’s trade. Each problem
and each dataset always requires a specific approach that cannot be
industrialized, which many people still don’t understand.
A fashion with ancient origins
A fashion with ancient origins
A fashion with ancient origins
A fashion with ancient origins
Key competencies
1. Prepare data (DB)
Recover, mix, enrich, filter, clean, verify, format, transform
data…
2. Models (ML/Stats)
Decision tree, regression, clustering, graphical model, SVM…
3. Interpret/share
(Visualisation)
Graphics, Data visualization, Maps…
Key competencies
1. Prepare data (DB) – 80% of the
job
Recover, mix, enrich, filter, clean, verify, format, transform
data…
2. Implementing a method
a model (ML/Stats)
Decision tree, regression, clustering, graphical model, SVM…
3. Interpret/share
(Visualisation) – 80% of the job
Graphics, Data visualization, Maps…
Key competencies
1. Data Munging
Retrieve, mix, enrich, filter, clean, verify, format, transform
data
2. Statistics
Traditional data analysis
3. Visualisation
Graphics, Data visualization, Maps…
Course Outline
-
handling R data with dplyr
-
introduction to visualization, good practices & common mistakes
-
ggplot and grammar of graphics
-
spatial data
-
introduction to cartography
